Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products

Rozen, Ohad; Carmel, David; Mejer, Avihai; Mirkis, Vitaly; Ziser, Yftah

doi:10.18653/v1/2021.naacl-main.23

Cited by 10 publications

(8 citation statements)

References 16 publications

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“…Column GPC means whether or not the data set follows the GPC taxonomy. regular multi-GPC size updated lingual shop family Farfetch product meta data [9] 400K Product details on Flipkart [3] 20K Amazon browse node classification [2] 3M Amazon product-question answering [16] 17.3GB Rakuten data challenge [10] 1M MAVE [18] 2.2M Innerwear from victoria's secret & co [15] 600K WDC-MWPD [19] 16K WDC-25 gold standard [14] 24K GreenDB [7] >576K…”

Section: Public E-commerce Data Setsmentioning

confidence: 99%

Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data

Flick¹,

Jäger²,

Trajanovska³

et al. 2023

Preprint

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Extracting structured information from unstructured data is one of the key challenges in modern information retrieval applications, including e-commerce. Here, we demonstrate how recent advances in machine learning, combined with a recently published multilingual data set with standardized fine-grained product category information, enable robust product attribute extraction in challenging transfer learning settings. Our models can reliably predict product attributes across online shops, languages, or both. Furthermore, we show that our models can be used to match product taxonomies between online retailers.

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Section: Public E-commerce Data Setsmentioning

confidence: 99%

Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data

Flick¹,

Jäger²,

Trajanovska³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…We start with AmazonPQA (Rozen et al, 2021), a publicly available dataset that contains product content including all the question-answers posted by customers and other product metadata from amazon.com. There can be multiple questions for a product and multiple answers for a question.…”

Section: Nli Model For Training Data Creationmentioning

confidence: 99%

Too much of product information : Don’t worry, let’s look for evidence!

Jain,

Rana,

Aggarwal

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

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Product question answering (PQA) aims to provide instant response to customer questions posted on shopping message boards, social media, brand websites and retail stores. In this paper, we propose a distantly supervised solution to answer customer questions by using product information. Auto-answering questions using product information poses two main challenges :(i) labelled data is not readily available (ii) lengthy product information requires attending to various parts of the text to answer the question. To this end, we first propose a novel distant supervision based NLI model to prepare training data without any manual efforts. To deal with lengthy context, we factorize answer generation into two sub-problems. First, given product information, model extracts evidence spans relevant to question. Then, model leverages evidence spans to generate answer. Further, we propose two novelties in fine-tuning approach: (i) First, we jointly fine-tune model for both the tasks in end-to-end manner and showcase that it outperforms standard multitask fine-tuning. (ii) Next, we introduce an auxiliary contrastive loss for evidence extraction. We show that combination of these two ideas achieves an absolute improvement of 6% in accuracy (human evaluation) over baselines.

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“…Ashok et al (2020) introduced a clustering approach to answer questions about products by accessing product reviews. Rozen et al (2021) examined the task of answering subjective and opinion questions when no (or few) reviews exist. Jiang et al (2010) proposed an opinion-based QA framework that uses manual question-answer opinion patterns.…”

Section: Opinionated Question Answeringmentioning

confidence: 99%

OpinionConv: Conversational Product Search with Grounded Opinions

Sadiri Javadi,

Potthast,

Flek

2023

Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue

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When searching for products, the opinions of others play an important role in making informed decisions. Subjective experiences about a product can be a valuable source of information. This is also true in sales conversations, where a customer and a sales assistant exchange facts and opinions about products. However, training an AI for such conversations is complicated by the fact that language models do not possess authentic opinions for their lack of realworld experience. We address this problem by leveraging product reviews as a rich source of product opinions to ground conversational AI in true subjective narratives. With Opinion-Conv, we develop the first conversational AI for simulating sales conversations. To validate the generated conversations, we conduct several user studies showing that the generated opinions are perceived as realistic. Our assessors also confirm the importance of opinions as an informative basis for decision making.

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Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products

Cited by 10 publications

References 16 publications

Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data

Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data

Too much of product information : Don’t worry, let’s look for evidence!

OpinionConv: Conversational Product Search with Grounded Opinions

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